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Document worth reading: “Artificial Neural Networks”

These are lecture notes for my course on Artificial Neural Networks that I have given at Chalmers (FFR135) and Gothenburg University (FIM720). This course describes the use of neural networks…

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Document worth reading: “Deep learning methods in speaker recognition: a review”

This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology….

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Document worth reading: “Transfer Adaptation Learning: A Decade Survey”

The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment….

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Document worth reading: “Artificial Intelligence: A Child’s Play”

We discuss the objectives of any endeavor in creating artificial intelligence, AI, and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best…

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Document worth reading: “Verification for Machine Learning, Autonomy, and Neural Networks Survey”

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent…

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Document worth reading: “A Large-Scale Comparison of Historical Text Normalization Systems”

There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder–decoder models,…

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Document worth reading: “A Tutorial on Kernel Density Estimation and Recent Advances”

This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features. We begin with a discussion of basic properties of KDE:…

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Document worth reading: “An Essay on Optimization Mystery of Deep Learning”

Despite the huge empirical success of deep learning, theoretical understanding of neural networks learning process is still lacking. This is the reason, why some of its features seem ‘mysterious’. We…

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Document worth reading: “Seven Myths in Machine Learning Research”

We present seven myths commonly believed to be true in machine learning research, circa Feb 2019. This is an archival copy of the blog post at Seven Myths in…

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Document worth reading: “Causality for Machine Learning”

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This…